Offline Handwritten Signature Verification - Literature Review (1507.07909v4)

Abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

• The paper presents a comprehensive analysis of offline signature verification methods by comparing classical techniques with emerging deep learning approaches.
• It highlights challenges such as high intra-class variability and limited genuine samples, emphasizing the importance of robust feature extraction.
• The review details the role of public datasets and evolving classifier models, offering insights toward more accurate and practical authentication systems.

An Academic Review of "Offline Handwritten Signature Verification - Literature Review"

The paper "Offline Handwritten Signature Verification - Literature Review" attempts to synthesize the extensive body of research in the discipline of handwritten signature verification, with a specific emphasis on offline methods. The task of signature verification is to authenticate a signature as genuine or classify it as a forgery, employing scanned images that are devoid of dynamic signing cues.

Main Contributions and Methodological Survey

The authors, Luiz G. Hafemann, Robert Sabourin, and Luiz S. Oliveira, offer a broad overview of past and present methodologies while acknowledging the persistent challenges posed by this area of paper. Among their primary contributions is the discussion of traditional versus modern approaches, particularly the integration of deep learning techniques over recent years.

1. Problem Formalization and Challenges: The paper outlines the primary challenge rooted in high intra-class variability and low inter-class variability, especially when dealing with skilled forgeries in signature verification. Moreover, they highlight constraints such as the limited number of genuine samples available per user during system training.
2. Datasets: The authors provide a comprehensive listing of public datasets pivotal for research, such as CEDAR, MCYT, and various GPDS datasets. The discussion notes the importance of public datasets for empirical evaluation and the establishment of benchmark results.
3. Feature Extraction Techniques: There is a robust emphasis on feature extraction methods, delineated into static, pseudo-dynamic, global, and local features. The paper explores a detailed account of geometric, graphometric, and directional features, alongside mathematical transformations, texture features, and interest point matching techniques like SIFT and SURF.
4. Deep Learning Advances: The paper highlights the application of deep learning, appreciating its capability in feature representation learning. Specifically, it discusses the use of CNNs to derive customized signature representations, which have shown efficacy across multiple benchmarks in tackling the signature variability challenge.
5. Classifier Models: Writer-dependent and writer-independent classification models are examined, with a focus on those utilizing dissimilarity representation for signature comparison. The paper covers several model types including HMMs, SVMs, and ensemble methods, underscoring their role in improving verification accuracy.

Implications and Future Directions

The research identifies data augmentation and feature learning via deep neural networks as fertile avenues to bolster system performance with limited training samples. Moreover, the development of ensemble methodologies and one-class classification paradigms are posited as promising strategies to combat the inherent challenges of signature variability and data scarcity.

From a theoretical standpoint, the paper discusses the importance of continued exploration into more robust feature extractors and the potential for learning adaptive feature representations that can generalize across diverse users and datasets. Practically, advancements in these areas could enhance the reliability of systems deployed in sensitive fields like banking and legal processes, where signature verification plays a pivotal role in identity security.

In sum, "Offline Handwritten Signature Verification - Literature Review" provides a noteworthy aggregation and critical analysis of the literature, setting the stage for ongoing innovations in offline signature verification strategies. The paper recognizes the continuing evolution of this discipline, driven by advancements in representation learning and the promise of deep learning methodologies, and predicts strides in both accuracy and practical applicability as new solutions emerge.